Introduction

Inventory management is no longer just about balancing stock levels—it is now driven by advanced analytics, AI-powered forecasting, and real-time optimization. Companies must leverage big data, machine learning, and predictive analytics to optimize inventory, minimize costs, and enhance supply chain agility.

This course provides cutting-edge strategies in inventory analytics, AI-driven optimization, demand forecasting, and risk mitigation. Participants will learn how to implement smart inventory solutions, reduce carrying costs, prevent stockouts, and enhance supply chain resilience.

Course Objectives

By the end of this course, participants will be able to:
✅ Utilize AI and predictive analytics for inventory forecasting.
✅ Implement real-time inventory tracking using IoT and blockchain.
✅ Optimize inventory levels with demand-driven and just-in-time (JIT) strategies.
✅ Reduce carrying costs, obsolescence, and shrinkage using analytics.
✅ Apply ABC analysis, EOQ models, and safety stock calculations effectively.
✅ Improve supply chain resilience with risk-based inventory strategies.
✅ Integrate advanced inventory optimization software and tools.

Who Should Attend?

This course is ideal for:

• Inventory & Supply Chain Managers
• Operations & Logistics Professionals
• Procurement & Demand Planning Specialists
• Warehouse & Distribution Center Managers
• Retail & E-Commerce Inventory Analysts
• Manufacturing & Production Planning Professionals
• Data Analysts & IT Professionals in Supply Chain

Day 1: Foundations of Inventory Analytics

• The Role of Inventory in Supply Chain Optimization
  • Inventory as a strategic asset for competitive advantage
  • Key metrics: inventory turnover, service levels, carrying costs
• Types of Inventory & Their Impact on Business
  • Raw materials, WIP, finished goods, MRO, and spare parts inventory
  • Understanding slow-moving, fast-moving, and obsolete stock
• Introduction to Inventory Analytics
  • Using data-driven decision-making for inventory control
  • Basics of statistical modeling, regression, and probability distributions

Day 2: Forecasting & Demand Planning with AI & Big Data

• Traditional vs. AI-Powered Demand Forecasting
  • Time-series forecasting, moving averages, and exponential smoothing
  • Machine learning models for demand prediction
• Big Data & Predictive Analytics in Inventory Management
  • Analyzing historical sales trends, seasonality, and external factors
  • Case Study: How Amazon optimizes inventory using AI
• Inventory Segmentation & Demand Classification
  • ABC/XYZ analysis for inventory prioritization
  • Decoupling points and postponement strategies

Day 3: Inventory Optimization Techniques & Strategies

• Just-in-Time (JIT) & Lean Inventory Management
  • Reducing waste, stockholding costs, and inefficiencies
  • Implementing Kanban and pull-based inventory systems
• Economic Order Quantity (EOQ) & Safety Stock Optimization
  • EOQ calculations for balancing order size and holding costs
  • Dynamic safety stock models for uncertain demand scenarios
• Multi-Echelon Inventory Optimization (MEIO)
  • Network-wide inventory balancing across multiple locations
  • Case Study: How Walmart optimizes multi-echelon inventory

Day 4: Smart Technologies & Risk-Based Inventory Strategies

• IoT & Blockchain for Real-Time Inventory Visibility
  • RFID, smart sensors, and digital twins for inventory tracking
  • Blockchain for secure, transparent inventory records
• Risk-Based Inventory Optimization & Disruption Management
  • Managing supply chain disruptions, shortages, and demand shocks
  • Scenario planning & simulation modeling for risk mitigation
• Sustainable & Circular Inventory Management
  • Reverse logistics, refurbished inventory, and waste reduction
  • Case Study: IKEA’s sustainable inventory practices

Day 5: Case Studies, Future Trends & Certification

• Emerging Trends in Inventory Analytics & Optimization
  • AI-powered self-learning inventory systems
  • The rise of predictive & prescriptive analytics in inventory management
• Case Studies: Best Practices in Inventory Optimization
  • Zara’s fast inventory turnover model vs. Tesla’s just-in-time strategy
  • Apple’s supply chain agility through inventory precision
• Capstone Project & Certification
  • Participants develop an inventory optimization strategy based on real-world data
  • Final assessment and feedback session
  • Certification: Certified Inventory Optimization Analyst (CIOA™)

Certification

Upon successful completion of the course and final assessment, participants will receive the Certified Inventory Optimization Analyst (CIOA™) credential. This certification validates expertise in AI-driven forecasting, inventory analytics, risk management, and supply chain optimization, enhancing career prospects in logistics, retail, and manufacturing.

Key Takeaways

✔ Master AI, machine learning, and predictive analytics in inventory management.
✔ Optimize inventory levels, demand forecasting, and supply chain resilience.
✔ Implement real-time tracking, IoT, and blockchain for inventory visibility.
✔ Reduce carrying costs, stockouts, and excess inventory with data-driven strategies.
✔ Earn an industry-recognized certification for career growth.